Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

The Mantel-Cox Log-Rank Test

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

Testing a Claim about Standard Deviation

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Modelling length-at-age relationships in fish: A simpler and more flexible alternative to the 'typical' von Bertalanffy and other common growth models.

Journal of fish biology·2026

Same author

Grass species and climatic season impact on Rhipicephalus microplus temporal abundance in a tropical region.

Pest management science·2026

Same author

The healthy human global project - Hong Kong: A community-based cross-sectional study of a healthy Asian population.

Cell reports. Medicine·2026

Same author

Mirror Therapy Versus Motor Imagery in Stroke Neurorehabilitation: A Systematic Review with Comparative Narrative Synthesis.

Life (Basel, Switzerland)·2026

Same author

Cross-sectional associations of sleep timing and eating behavior factors with migraine in a predominantly female adult sample.

Chronobiology international·2026

Same author

Stroke Neurorehabilitation and the Role of Motor Imagery Training: Do ARAT and Barthel Index Improvements Support Its Clinical Use? A Systematic Review and Meta-Analysis.

Medicina (Kaunas, Lithuania)·2026

Search research articles

Related Experiment Video

Updated: Sep 24, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Logistic versus linear regression-based reliable change index: A simulation study with implications for clinical

Rafael De Andrade Moral¹, Unai Díaz-Orueta², Javier Oltra-Cucarella³

¹Department of Mathematics and Statistics, Maynooth University.

Psychological Assessment

|May 5, 2022

Summary

The logistic reliable change index (RCI) is more accurate for detecting memory impairments than the linear RCI. The linear RCI becomes reliable with sample sizes of 200 or more.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

Related Experiment Videos

Last Updated: Sep 24, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

Area of Science:

Neuroscience
Psychology
Biostatistics

Background:

The reliable change index (RCI) is crucial for assessing memory changes over time.
Determining the minimum sample size for reliable RCI estimates is essential but previously unspecified.

Purpose of the Study:

To determine the minimum sample size for reliable linear regression-based reliable change index (RCI) estimates.
To compare the accuracy of linear RCI with a logistic RCI for discrete, bounded scores.

Main Methods:

Simulated 12,000 datasets with sample sizes ranging from 10 to 1,000, using Alzheimer's Disease Neuroimaging Initiative data.
Analyzed estimate significance, coverage rates, and model accuracy (true-positive/true-negative rates).
Compared linear RCI against a logistic RCI for discrete scores.

Main Results:

The logistic RCI demonstrated higher overall accuracy compared to the linear RCI.
Linear RCI estimates approached the accuracy of the logistic RCI with sample sizes of 200 or greater.
Provided an R package for computing the logistic RCI and code for result reproduction.

Conclusions:

The logistic RCI is recommended for improved accuracy in detecting memory impairments.
Researchers should consider sample size when utilizing the linear RCI for longitudinal memory assessments.